26582 results about "Software" patented technology

An example operation may include one or more of receiving an input from a user during a conversation that includes a plurality of prompts between the user and a chatbot within a chat window of a software application, converting text content within the received input into a vector, executing a large language model (LLM) on the vector and a database of vectorized responses to identify a vectorized response to output from among the plurality of vectorized responses within the database, converting the vectorized response into a text response, and displaying the text response output by the chatbot within the chat window of the software application.

The invention provides a multi-source software supply chain intelligent analysis method and system. The method comprises the following steps: analyzing software component identification information submitted by a user, extracting attribute characteristics, positioning node paths among components, and generating a weighted dependency chain list containing direct and indirect dependency chains; a Bayesian network model is combined with a predefined risk index system, the risk score of each dependency relationship chain is calculated, potential supply chain threat points are identified through an anomaly detection algorithm, and a risk report is generated; the report covers the overall risk score and the threat point of the key link or component; based on the report, drawing a comprehensive risk map by using a multi-dimensional visualization algorithm, and displaying component groups with similar risk modes; and a genetic algorithm and a simulated annealing algorithm are fused to explore a high-risk area, an adjustment suggestion is proposed, and a risk mitigation strategy is generated to reduce the overall risk score. According to the technical scheme provided by the embodiment of the invention, the security and integrity of the multi-source software supply chain are improved.

An example operation may include one or more of receiving a sequence of inputs from a user during a conversation between the user and a chatbot within a chat window of a software application, executing a large language model (LLM) on each input from the user to determine a next prompt to output via the chatbot, respectively, wherein each execution of the LLM includes a new chat input from the user and a most-recent state of the conversation between the user and the chatbot within the chat window, and displaying the next prompt within a chat window on a user device.

An example operation may include one or more of executing tests on a software application via a test environment of a test platform and logging results of the tests in a log file, identifying a new feature to be added to the software application based on execution of a machine learning model on logged results of the tests stored in the log file, generating source code for the new feature to be added to the software application based on execution of a generative artificial intelligence (GenAI) model on the new feature and a repository of source code, and displaying the generated source code via a user interface of the software application.

An example operation may include one or more of monitoring communications that occur with user devices over a shared computer network, detecting a security threat from the monitored communications, generating a software program to simulate the security threat over the shared computer network based on execution of a generative artificial intelligence (GenAI) model on a description of the security threat and a repository of source code, installing the source code for simulating the security threat on a system associated with the computer network, and executing the source code for simulating the security threat via the system.

An example operation may include one or more of storing a generative artificial intelligence (GenAI) model configured to create images, displaying a plurality of prompts on a user interface of a software application, receiving an identifier of a goal of the user and attributes of the goal via the plurality of prompts on the user interface, executing the GenAI model on the identifier of the goal of the user and the attributes of the goal to generate a custom image of the goal for the user, and displaying the custom image of the goal via the user interface of the software application.

The present disclosure relates to computer-implemented methods, software, and systems for transferring contexts from a generic chatbot to a domain-specific chatbot. In one example, a registration at a first conversational interface is received that identifies at least one endpoint associated with a first entity that is separate from the first conversational interface and is associated with a domain-specific conversational interface. The first conversational interface can interact with a user through a natural language search interaction. The interface can identify at least one response associated with one of the at least one endpoints associated with the first entity, and can provide the response and an interactive response associated with initiating a transfer of the interaction from the first conversational interface to a domain-specific conversational interface of the first entity. Once accepted, the interaction is transferred to the first entity and its domain-specific conversational interface.

The invention provides an information network security self-defense method and system based on trusted computing, and the method comprises the steps: constructing a trusted computing platform which comprises the steps: deploying a trusted platform module TPM, building a trusted software stack TSS, and implementing a trusted network connection TNC; designing a self-defense architecture, wherein the self-defense architecture comprises deployment of a network layer, an application layer and a data layer defense system, integration of an intelligent sensing system and establishment of a central control and decision center; network traffic and user access behaviors are monitored, and embedded attack behaviors are preprocessed; performing anomaly detection on the network flow and the user access behavior by applying a machine learning algorithm; calculating a real-time risk score based on the anomaly detection result, and generating a security situation awareness report; and based on the security situation awareness report, defining a threat level and a response level, and executing automatic defense.

An example operation may include one or more of receiving a first code base and a second code base from a software repository, wherein each of the first and second code bases comprise source code of a plurality of different software programs, identifying a source code within the first code base that is interdependent with a source code within the second code base based on execution of a generative artificial intelligence (GenAI) model on the first and second code bases, determining a software component to connect the source code within the first code base to the source code within the second code base, and displaying an identifier of the software component via a user interface.

An example operation may include one or more of ingesting data from one or more websites via one or more application programming interfaces (APIs) and storing the data within a data store of a host platform, displaying one or more prompts on a user interface of a user profile page within a software application hosted by the host platform, receiving one or more responses to the one or more prompts, generating a travel itinerary based on execution of an artificial intelligence (AI) model on the data from the one or more websites, the one or more prompts, and the one or more responses, and displaying the travel itinerary via the user interface of the user profile page of the software application.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model based on execution of the GenAI model on software libraries and descriptions of intent of the software libraries, receiving a first set of software libraries and a second set of software libraries, identifying a first software library within the first set of software libraries that includes redundant functionality with a second software library within the second set of software libraries based on execution of a generative artificial intelligence (GenAI) model on the first and second sets of libraries, and displaying an identifier of the first and second software libraries via a user interface.

A generative artificial intelligence (AI) driven code fixing pipeline has been created that uses a transformer-based large language model (LLM) to patch flawed program code. A pre-trained LLM is fine-tuned to generate a response that is a modified version of a code fragment in a prompt to the pre-trained model. After fine-tuning, the pre-trained LLM (hereinafter “code fix model”) is integrated into a pipeline that includes a program code cybersecurity scanner and a prompt generator. The scanner generates indications of flaws in program code and weakness types for those flaws. These indications flow into the prompt generator. The prompt generator retrieves reference code pairs based on weakness type and programming language to generate a batch of prompts to run inference on with the code fix model. The responses generated by the code fix model are presented as patching alternatives.

An example operation may include one or more of ingesting profile data from one or more websites via one or more application programming interfaces (APIs) and storing the profile data within a data store, displaying one or more prompts on a user interface of a user profile page within a software application hosted by a host platform, receiving one or more responses to the one or more prompts, determining a goal for a user of the user profile page based on execution of an artificial intelligence (AI) model on the profile data, the one or more prompts, and the one or more responses to the one or more prompts, and displaying an identifier of the goal via the user interface of the user profile page of the software application.

An example operation may include one or more of training a large language model (LLM) to learn credit card data via execution of the LLM on content from one or more credit card documents, executing the LLM to generate a sequence of prompts which are output to a user via a chatbot within a chat window of a software application, receiving responses to the sequence of prompts for the user via the chat window of the software application, and retraining the LLM model to further learn credit card data via execution of the LLM on a combination of the sequence of prompts and the received responses.

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

An example operation may include one or more of storing a data store of credit card documentation, conversing with a user via a chatbot within a chat window of a software application, wherein the conversing comprises receiving natural language inputs with requests for information about a payment card, identifying a benefit obtained by the user based on the conversation, executing a large language model (LLM) on the identified benefit and the data store of the credit card documentation to generate a letter of confirmation, and generating an electronic message with the letter of confirmation attached, and transmitting the electronic message to a user device.

An example operation may include one or more of displaying a report on a user interface of a software application on a user device, listening to a call between a user on the user device and a different user on a second user device that is connected to the user device via a network, executing a generative artificial intelligence (GenAI) model based on content that is heard during the call and content within the report displayed on the user interface to identify content within the displayed report that is discussed during the call, and modifying the displayed report to emphasize the identified content within the displayed report on the user interface.

An example operation may include one or more of establishing a network connection between a computing system and one or more external sources over a computer network, receiving a request from a user via a software application on a user device, collecting data about the user from the one or more external sources via the established network connection, executing a machine learning model on the collected data about the user to determine a goal of the user, and displaying an image of the goal via a user interface of the software application.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model to generate images based on user data using a dataset of images, executing the GenAI model based on input data from a user interface of a software application to generate an image corresponding to the input data, and displaying the image via a user interface of a software application, receiving feedback about the image via the user interface; andretraining the GenAI model based on the generated image and the received feedback about the image via the user interface.

An example operation may include one or more of training an artificial intelligence (AI) model to recommend numerical goals based on execution of the AI model on historical profile data and contextual data that is associated with the historical profile data, storing profile data of a user profile within a data store of a software application, receiving a request via a user interface of a user profile page of the software application, wherein the request comprises context of a user of the user profile, determining a numerical goal for the user profile based on execution of the AI model on the profile data of the user profile and the context, and displaying the numerical goal via the user interface of the user profile page within the software application.

An example operation may include one or more of storing user data in a storage device, receiving inputs via prompts displayed on a user interface of a software application, identifying a plurality of goals within the received inputs and generating a plurality of data structures corresponding to the plurality of goals, respectively, executing a generative artificial intelligence (GenAI) model on the plurality of goals and the stored user data to generate a plurality of custom images of the plurality of goals, and displaying a plurality of identifiers of the plurality of data structures corresponding to the plurality of goals on a user interface, and simultaneously display the plurality of custom images next to the plurality goals, respectively.

An example operation may include one or more of ingesting data from one or more websites via one or more application programming interfaces (APIs) and storing the data within a data store of a host platform, generating one or more prompts and displaying the one or more prompts on a user interface of a user profile page within a software application hosted by the host platform, receiving one or more responses to the one or more prompts, generating a design of a structure based on execution of an artificial intelligence (AI) model on the data from the one or more websites, the one or more prompts, and the one or more responses, and displaying the design of the structure via the user interface of the user profile page of the software application.

A server computer system comprises a communications module; at least one processor coupled with the communications module; and a memory coupled to the processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to provide a plug-in to an interface design software application executing on a computing device, the plug-in allowing the server computer system to communicate with the computing device to monitor design of an interface within the interface design software application; generate at least one recommendation for tagging at least one element of the interface; and send, for display on a display screen of the computing device within the interface design software application, the at least one recommendation for tagging the at least one element.

The present invention discloses a software platform designed to facilitate seamless interconnection between users and multiple applications in an application-agnostic and centralized manner. This novel system simplifies the user experience by providing a unified interface for receiving notifications from, and interacting with, a multitude of disparate applications. Central to the platform is a robust middleware that bridges communication gaps, eliminating the need for manual data reconciliation and ensuring streamlined operations.Key components of the system include an Ingester Module that standardizes data from various sources, an Action Card Creator that transforms this data into interactive notifications, a Notification Dispatcher that routes these notifications to appropriate platforms, and a Command Translation Module that interprets user interactions for actionable insights. The platform offers interoperability across various communication mediums, employing AI and machine learning for enhanced data processing, predictive personalization, and efficient notification dispatching.Aimed at enhancing operational efficiency, the system addresses challenges faced by enterprises due to fragmented application environments, offering a scalable solution that adapts to organizational needs. By centralizing communication and workflow management, the disclosed invention provides businesses with a consolidated approach to managing multiple application interactions, driving innovation, and reducing technical complexities.

The invention discloses a self-adaptive software vulnerability repair system based on deep learning. The self-adaptive software vulnerability repair system comprises a multi-modal feature fusion engine, a spatio-temporal joint modeling module, a vulnerability repair strategy generator and a cross-language representation model. The multi-modal feature fusion engine generates a mixed feature vector of a code, inputs the mixed feature vector into the spatio-temporal joint modeling module, captures code structure and time sequence dependence, and identifies risk features; the vulnerability repair strategy generator generates a repair scheme based on the risk features and optimizes the repair scheme through static verification; and the cross-language representation module performs feedback training by using the newly added sample and outputs a final detection result and a repair scheme. According to the method, the vulnerability trigger points can be accurately positioned, all the associated code snippets are extracted, a solid foundation is provided for subsequent vulnerability analysis, and the repaired codes conform to grammatical rules and keep original semantic consistency.

An example operation may include one or more of receiving context of a user during an inquiry of a feature via a software application, executing a waiting period via the software application, during the waiting period, selecting an animation to display via the software application based on the context of the user and the feature inquiry wherein the animation provides contextual data associated with the feature, wherein the contextual data is based on a determined need of the user, displaying the animation via the software application during the waiting period, and determining if the user has accepted the feature via the software application. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

An example operation may include one or more of training an artificial intelligence (AI) model based on a plurality of dashboards related to a software application that corresponds to a plurality of topics, ingesting a call transcript from a previous call with a user, generating a new topic from the call transcript, determining that the new topic is distinct from the existing plurality of topics, executing the AI model based on the new topic, generating a dashboard with content based on the execution of the AI model, and displaying the dashboard via the software application.

Examples of the present disclosure describe systems and methods for automatically detecting and repairing reliability issues in operating systems and applications using a generative artificial intelligence (“AI”) system. In examples, a generative AI system receives a request to evaluate a detected issue in a software service or application. In response, the system analyzes error information associated with the detected issue to build an error context for the software code that caused the detected issue. The error context is used to identify the location of the software code file that comprises the software code. The error context and the software code file are used to identify a prompt. The prompt, the error context, and / or the identified software code are provided as input to a language model. The language model provides an output that is responsive to the user request and may perform actions to further evaluate or repair the detected issue.

An example operation may include one or more of executing a waiting period of time within a software application being accessed by a user, executing an animation via the software application during the waiting period, determining a result of the software application being accessed by the user, determining additional time to add to the waiting period of time based on the result, via the software application, executing the waiting period with the additional time, augmenting the animation based on the additional time to add to the waiting period, via the software application, and executing the augmented animation via the software application during the additional time. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

An example operation may include one or more of registering a user for a first feature by a software application, wherein the registering of the user comprises receiving information about the user, determining that the received information does not meet a first condition related to a first feature, determining that the received information meets a second condition related to a second feature, generating an offer to the user the second feature based on the information, detecting acceptance of the second feature, by the software application, and enabling the second feature by the software application for the user in response to the acceptance. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.